Latest AI and machine learning research in surveys for healthcare professionals.
OBJECTIVE: Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of subgroup performance disparities. However, since not all sources of bias in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess their impacts. In this article, we introduce an analysis framework for systematically and obje...
What predicts cross-country differences in the recovery of socioeconomic activity from the COVID-19 pandemic? To answer this question, we examined how quickly countries' socioeconomic activity bounced back to normalcy from disruptions caused by the COVID-19 pandemic based on residents' attitudes, values, and beliefs as measured in the World Values Survey. We trained nine preregistered machine lear...
In the drug discovery process, the low success rate of drug candidate screening often leads to insufficient labeled data, causing the few-shot learn...
For critical applications that require a higher level of reliability, the Triple Modular Redundancy (TMR) scheme is usually employed to implement fa...
In Byzantine Agreement (BA), there is a set of $n$ parties, from which up to $t$ can act byzantine. All honest parties must eventually decide on a c...
In this paper, we present a comprehensive survey on the pervasive issue of medical misinformation in social networks from the perspective of informa...
Stereotypical bias encoded in language models (LMs) poses a threat to safe language technology, yet our understanding of how bias manifests in the p...
The presence of unhealthy nodes in cloud infrastructure signals the potential failure of machines, which can significantly impact the availability a...
Clinical decision-making relies heavily on causal reasoning and longitudinal analysis. For example, for a patient with Alzheimer's disease (AD), how...
Efforts to mitigate bias and enhance fairness in the artificial intelligence (AI) community have predominantly focused on technical solutions. While...
Deep neural networks (DNNs) suffer from the spectral bias, wherein DNNs typically exhibit a tendency to prioritize the learning of lower-frequency c...
As a cornerstone of modern information access, search engines have become indispensable in everyday life. With the rapid advancements in AI and natu...
Text-to-Image generative systems are progressing rapidly to be a source of advertisement and media and could soon serve as image searches or artists...
News outlets, surveyors, and other organizations often conduct polls on social networks to gain insights into public opinion. Such a poll is typical...
For decades, the Bj{\o}ntegaard Delta (BD) has been the metric for evaluating codec Rate-Distortion (R-D) performance. Yet, in most studies, BD is d...
Knowledge Distillation is a commonly used Deep Neural Network (DNN) compression method, which often maintains overall generalization performance. Ho...
Text-To-Image (TTI) Diffusion Models such as DALL-E and Stable Diffusion are capable of generating images from text prompts. However, they have been...
Purpose: In radiology, large language models (LLMs), including ChatGPT, have recently gained attention, and their utility is being rapidly evaluated...
Machine learning models can capture and amplify biases present in data, leading to disparate test performance across social groups. To better unders...
Objective: To improve prediction of Chronic Kidney Disease (CKD) progression to End Stage Renal Disease (ESRD) using machine learning (ML) and deep ...